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Discover Data is a peer-reviewed journal published by Springer Nature, covering Natural Language Processing Techniques, Imbalanced Data Classification Techniques, Topic Modeling.
| Publisher | Springer Nature |
|---|---|
| Reference style | Superscript numbered (Nature) Superscript — small raised numerals in the text 1. Smith, A., Jones, B. & Lee, C. A representative article title. Discover Data 12, 45–58 (2023).
Formats any DOI in Discover Data style. No sign-up. |
| Publishes research in | Natural Language Processing Techniques Imbalanced Data Classification Techniques Topic Modeling Network Security and Intrusion Detection Big Data and Business Intelligence |
| ISSN | 2731-6955 |
| Citation impact (2-yr) | 2.13 |
| h-index | 8 |
| i10-index | 6 |
| Total citations | 225 |
| Open access | Yes |
| Top institutions publishing here | University of Notre Dame |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Abstract As the landscape of big data evolves, the paradigm of data sharing and exchanging has gained paramount importance. Nonetheless, the transition to efficient data sharing and exchanging is laden with challenges. One of the principal challenges is incentivizing diverse users to partake in the data sharing and exchange process. Users, especially those in potential…
Abstract Machine learning (ML) is playing an increasingly important role in rendering decisions that affect a broad range of groups in society. This posits the requirement of algorithmic fairness , which holds that automated decisions should be equitable with respect to protected features (e.g., gender, race). Training datasets can contain both class imbalance and protected…
Sindhi, a low-resource language spoken by millions, faces significant challenges in Natural Language Processing (NLP) due to the scarcity of annotated datasets. This paper presents DAugSindhi, a study focused on enhancing Sindhi text classification through data augmentation techniques. These methods aim to address data scarcity by artificially expanding the dataset to improve model performance. The…
Research into Intrusion and Anomaly Detectors at the Host level typically pays much attention to extracting attributes from system call traces. These include window-based, Hidden Markov Models, and sequence-model-based attributes. Recently, several works have been focusing on sequence-model-based feature extractors, specifically Word2Vec and GloVe, to extract embeddings from the system call traces due to their…
Abstract Google Trends is a popular data source that has been utilized in hundreds of studies across various fields, including information technology, business, economics, healthcare, and political science. While several previous research has addressed sampling error issues, this article focuses on the measurement errors resulting from changes in Google Trends' data collection method. By examining…
